{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "print(iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "replace the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=[re.sub(\"[() ]\", \"\", col) for col in df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris.target_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "add the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['species']=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallengthcm</th>\n",
       "      <th>sepalwidthcm</th>\n",
       "      <th>petallengthcm</th>\n",
       "      <th>petalwidthcm</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepallengthcm  sepalwidthcm  petallengthcm  petalwidthcm  species\n",
       "0            5.1           3.5            1.4           0.2        0\n",
       "1            4.9           3.0            1.4           0.2        0\n",
       "2            4.7           3.2            1.3           0.2        0\n",
       "3            4.6           3.1            1.5           0.2        0\n",
       "4            5.0           3.6            1.4           0.2        0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Min-max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.22222222, 0.16666667, 0.11111111, 0.08333333, 0.19444444,\n",
       "       0.30555556, 0.08333333, 0.19444444, 0.02777778, 0.16666667])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessing.minmax_scale(df['sepallengthcm'])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm = preprocessing.MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallengthcm</th>\n",
       "      <th>sepalwidthcm</th>\n",
       "      <th>petallengthcm</th>\n",
       "      <th>petalwidthcm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.084746</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.067797</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepallengthcm  sepalwidthcm  petallengthcm  petalwidthcm\n",
       "0       0.222222      0.625000       0.067797      0.041667\n",
       "1       0.166667      0.416667       0.067797      0.041667\n",
       "2       0.111111      0.500000       0.050847      0.041667\n",
       "3       0.083333      0.458333       0.084746      0.041667\n",
       "4       0.194444      0.666667       0.067797      0.041667"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(mm.fit_transform(df[columns]), columns=columns).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- z-score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallengthcm</th>\n",
       "      <th>sepalwidthcm</th>\n",
       "      <th>petallengthcm</th>\n",
       "      <th>petalwidthcm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.900681</td>\n",
       "      <td>1.019004</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-0.131979</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.385353</td>\n",
       "      <td>0.328414</td>\n",
       "      <td>-1.397064</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.506521</td>\n",
       "      <td>0.098217</td>\n",
       "      <td>-1.283389</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.021849</td>\n",
       "      <td>1.249201</td>\n",
       "      <td>-1.340227</td>\n",
       "      <td>-1.315444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepallengthcm  sepalwidthcm  petallengthcm  petalwidthcm\n",
       "0      -0.900681      1.019004      -1.340227     -1.315444\n",
       "1      -1.143017     -0.131979      -1.340227     -1.315444\n",
       "2      -1.385353      0.328414      -1.397064     -1.315444\n",
       "3      -1.506521      0.098217      -1.283389     -1.315444\n",
       "4      -1.021849      1.249201      -1.340227     -1.315444"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(scaler.fit_transform(df[columns]), columns=columns).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nm = preprocessing.Normalizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepallengthcm</th>\n",
       "      <th>sepalwidthcm</th>\n",
       "      <th>petallengthcm</th>\n",
       "      <th>petalwidthcm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.803773</td>\n",
       "      <td>0.551609</td>\n",
       "      <td>0.220644</td>\n",
       "      <td>0.031521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.828133</td>\n",
       "      <td>0.507020</td>\n",
       "      <td>0.236609</td>\n",
       "      <td>0.033801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.805333</td>\n",
       "      <td>0.548312</td>\n",
       "      <td>0.222752</td>\n",
       "      <td>0.034269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.800030</td>\n",
       "      <td>0.539151</td>\n",
       "      <td>0.260879</td>\n",
       "      <td>0.034784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.790965</td>\n",
       "      <td>0.569495</td>\n",
       "      <td>0.221470</td>\n",
       "      <td>0.031639</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepallengthcm  sepalwidthcm  petallengthcm  petalwidthcm\n",
       "0       0.803773      0.551609       0.220644      0.031521\n",
       "1       0.828133      0.507020       0.236609      0.033801\n",
       "2       0.805333      0.548312       0.222752      0.034269\n",
       "3       0.800030      0.539151       0.260879      0.034784\n",
       "4       0.790965      0.569495       0.221470      0.031639"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(nm.fit_transform(df[columns]), columns=columns).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = sns.load_dataset('iris')\n",
    "sns.pairplot(iris)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
